import torch.utils.model_zoo as model_zoo
import torch
import torch.nn as nn

from scipy import misc
import math
import torch.nn.functional as F

from torchvision.models.resnet import ResNet
from torchvision.models.resnet import BasicBlock
from torchvision.models.resnet import Bottleneck


from .base_model import BaseModel

# __all__ = ['ResNet']

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}

def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class ResNetModel(BaseModel):
    def __init__(self, opt):
        super(ResNetModel, self).__init__()

        self.rn = opt.rn



        print("[LOG]:Input board size is {}*{}, using ResNet-{}.".format(self.n,self.n,18))
            self.main_model = self.resnet18(self.game,self.args)
        else:
            print("[LOG]:Input board size is {}*{}, using ResNet-{}.".format(self.n,self.n,34))
            self.mani_model = self.resnet34(self.game,self.args)

    def resnet18(self,game,args,pretrained=False):
        model = AlphaNet(game,args, [2, 2, 2, 2])
        if pretrained:
            model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
        return model


    def resnet34(self,game,args,pretrained=False, **kwargs):
        model = AlphaNet(game,args, [3, 4, 6, 3])
        if pretrained:
            model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
        return model



class AlphaNet(ResNet):
    def __init__(self, game,args,layers):
        block=AlphaBlock
        self.board_x, self.board_y = game.getBoardSize()
        self.action_size = game.getActionSize()
        self.args = args
        outputShift=1 if self.board_x in [6,7,11] else 4
        self.inplanes = 64

        super(ResNet, self).__init__()

        self.conv1 = nn.Conv2d(1, 64, kernel_size=5, stride=1, padding=2,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=5, stride=1, padding=2)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(2, stride=1)
        self.fc_p= nn.Linear(512 * block.expansion*outputShift,self.action_size)
        self.fc_v=nn.Linear(512* block.expansion*outputShift,1)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)


    def forward(self, x):
        x= x.view(-1, 1, self.board_x, self.board_y)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        try:
            x = self.avgpool(x)
        except:
            pass
        x = x.view(x.size(0), -1)
        p = self.fc_p(x)
        v = self.fc_v(x)
        return F.log_softmax(p,dim=1),F.tanh(v)


    



class AlphaBottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(AlphaBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out




class AlphaBlock(nn.Module):
    expansion = 1
    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(AlphaBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

